Introducing: Bob Williamson

Professor Bob Williamson is the leader of the Machine Learning group at NICTA.

He received a Bachelor of Electrical Engineering from the Queensland University of Technology in 1984 and a Masters of Engineering Science (Electrical Engineering) from the University of Queensland in 1986. In 1990 he obtained a PhD in Electrical Engineering from the University of Queensland. He is a fellow of the Australian Academy of Science. He joined the Australian National University as a postdoctoral fellow in the Department of Systems Engineering in 1990 and held a series of appointments before becoming a professor and head of the Computer Sciences Laboratory, Research School of Information Sciences and Engineering at the Australian National University. From 2003 to early 2006 Professor Williamson was the Director of NICTA’s Canberra Research Laboratory. In 2006 he was appointed as NICTA's Scientific Director. Since 2011 he has been leading the Machine Learning group. He is a member of the advisory board of the National Institute of Informatics (Japan) and was previously a member of the Scientific Advisory Board of the Max-Planck Institute for Biological Cybernetics. His scientific interests include signal processing and machine learning.

He will be presenting on Machine Learning and Big Data and will give an overview of machine learning problems that arise in the area of big data. He will present some example projects that NICTA is using machine learning to solve a range of real-world problems, and outline some of the exciting research challenges that remain.

Tags: 

Add new comment

Filtered HTML

  • Web page addresses and e-mail addresses turn into links automatically.
  • Allowed HTML tags: <a> <em> <strong> <cite> <blockquote> <code> <ul> <ol> <li> <dl> <dt> <dd>
  • Lines and paragraphs break automatically.

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Image CAPTCHA
Enter the characters shown in the image.